1. Field of the Invention
The present invention relates to the fields of synthetic aperture radar, fuze radar, collision avoidance radar, and precision approach and landing radar.
2. Prior Art
Synthetic Aperture Radar (SAR) uses a series of radar pulses transmitted and received over time from a moving platform to create a range-crossrange image. The bandwidth of the radar provides range resolution and the angular rotation of the radar line of sight with respect to the scene to be imaged provides crossrange resolution. The formation of a perfectly focused image requires accounting for the time-varying range to each point in the scene, which variation differs from point to point. Most widely used SAR image formation algorithms (Polar Format Algorithm, Range Doppler Algorithm, Chirp Scaling Algorithm, Back Projection Algorithm) approximate the range variation in ways that work to varying degrees for side-looking SAR, but work poorly for forward-looking SAR. That is, these algorithms do not provide a well-focused image of the region toward which the platform is moving. (See Carrara, W. G., R. S. Goodman, and R. M. Majewski, Spotlight Synthetic Aperture Radar: Signal Processing Algorithms, Artech House, 1995; Cumming, I. G. and F. H. Wong, Digital Processing of Synthetic Aperture Radar Data: Algorithms and Implementation, Artech House, 2005; and Jakowatz, C. V., et al., Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach, Kluwer Academic, 1996.)
Only one of the general approaches to SAR imaging, the Range Migration or Omega-K Algorithm, avoids approximations that are invalid for forming an image of the region toward which the platform is moving. (See Carrara, W. G., R. S. Goodman, and R. M. Majewski, Spotlight Synthetic Aperture Radar: Signal Processing Algorithms, Artech House, 1995; Cumming, I. G. and F. H. Wong, Digital Processing of Synthetic Aperture Radar Data: Algorithms and Implementation, Artech House, 2005; and Soumekh, M., Synthetic Aperture Radar Signal Processing with MATLAB Algorithms, John Wiley & Sons, 1999.) General descriptions of this algorithm assert that it applies for any squint angle, but the signal and image processing literature indicates that when it is implemented for high squint angles (forward-looking) it may in practice produce responses that are aberrated, wide, or shifted from their true locations. (See Cadalli, N. and D. C. Munson Jr., “A Simulation Study of the ω-k SAR Algorithm for the Highly Squinted Case with Application to Runway Imaging”, Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, Istanbul, Turkey, Jun. 5-9, 2000, vol. 5, pp. 3025-3028; and Cadalli, N. and D. C. Munson Jr., “A comparison of ω-k and generalized SAR inversion for runway imaging”, Proc. IEEE Int. Conf. Image Processing, Vancouver, BC, Canada, Sep. 10-13, 2000, vol. 1, pp. 693-696.) Moreover, the algorithm requires a time-consuming processing step (Stolt interpolation) that cannot be executed until all the data used to form the image have been collected, which makes the algorithm poorly suited for use in real time by fast-moving platforms; data collection must cease at such a long range from an object to be imaged that only crude crossrange resolution can be obtained. Approximate versions of the Omega-K Algorithm replace Stolt interpolation with a faster step, but this replacement works poorly for forward-looking SAR (see Cumming, I. G. and F. H. Wong, Digital Processing of Synthetic Aperture Radar Data: Algorithms and Implementation, Artech House, 2005).